The importance of accurate recommender systems has been widely recognized by academia and industry. How- ever, the recommendation quality is still rather low. Recently, a linear sparse and low-rank representation of the user-item matrix has been applied to produce Top-N recommendations. This approac... || 非凸秩估计; slim; || Zhao Kang Qiang Cheng...
Top-N recommender systems have been investigated widely both in industry and academia. However, the recommenda- tion quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item ma- trix based on a low-rank assumption and simultaneously keep the... || SLIM; nuclear; LorSLIM; augmented Lagrangian multiplier (ALM) method; logdet; 秩约束; nonconvex relaxation; convex relaxation; 非凸松弛;; || Zhao Kang Chong Peng Qiang Cheng...
Learning to rank (L2R) algorithms use a labeled training set to generate a ranking model that can be later used to rank new query results. These training sets are very costly and laborious to produce, requiring human annotators to assess the relevance or order of the documents in relation to a query... || L2R; IR; AL; Active Learning (主动学习); 半监督学习; 直推学习(transductive learning); || Rodrigo M. Silva, Guilherme C. M. Gomes, Mário S. Alvim, Marcos A. Gonçalves...
主要介绍下L2R在IR领域中的应用,尤其区别于L2R在RecSys中的应用.
Information Retriveal with Learning to Rank (problem setting)... || LETOR; L2R: IR; || Tie-Yan Liu and Hang Li...
Item recommendation is the task of predict- ing a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common sce- nario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback ... || BPR; pairwise; 抑制过拟合使得排序成为可能; || Steffen Rendle, Christoph Freudenthaler, Zeno Gantner and Lars Schmidt-Thieme...
Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest- descent minimization. A general gradient descent “boosting” paradigm is developed for ad... || 4000 citations; gradient boosting; || Jerome H. Friedman...
Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boostin... || Ensemble; 3000 citions; 集成学习; boosting; bootstrap; bagging (bootstap aggregating); gradient boosting; Adaboost 抽样方法; || Thomas G Dietterich...
With rapid growth of information on the Internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information can be used as a significant factor in modeling user behavior, various context-aware recommendation methods are propo... || MF; Tensor; nlp; || Qiang Liu, Shu Wu, Liang Wang...
